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Fuel Consumption Estimation of Potential Driving Paths by Leveraging Online Route APIs

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11204)


Greenhouse gas and pollutant emissions generated by an increasing number of vehicles have become a significant problem in modern cities. Estimating fuel usage of potential driving paths can help drivers choose fuel-efficient paths to save more energy and protect the environment. In this paper, we build a fuel consumption model (FCM) for drivers based on their historical GPS trajectory and OBD-II data. FCM on a path only needs three parameters (i.e., the path distance, traveling time on the path and path curvature), which can be easily obtained from online route APIs. Based on experiment results, we can conclude that the proposed model can achieve high accuracy, with a mean fuel consumption error of less than 10% for paths longer than 15 km. In addition, the traveling time on paths provided by online route APIs is accurate and can be input into FCM to estimate the fuel usage of paths.


  • GPS trajectories
  • OBD-II
  • Online route APIs
  • Fuel consumption model

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    The sampling time of the GPS trajectory data is 6 s; the sampling time of the OBD-II data is 10 s.

  2. 2.

    Both road network and physical features are extracted from OpenStreetMap, with more details can be found at

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The work was supported by the National Science Foundation of China (No. 61602067), the Fundamental Research Funds for the Central Universities (No. 106112017cdjxy180001), Chongqing Basic and Frontier Research Program (No. cstc2015jcyjA00016), Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University).

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Correspondence to Chao Chen .

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Ding, Y., Chen, C., Xie, X., Yang, Z. (2019). Fuel Consumption Estimation of Potential Driving Paths by Leveraging Online Route APIs. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15092-1

  • Online ISBN: 978-3-030-15093-8

  • eBook Packages: Computer ScienceComputer Science (R0)